System and method for metadata transfer among search entities

ABSTRACT

A new approach is proposed that contemplates systems and methods to ascribe or transfer metadata from one search-related entity to another, where each entity can be one of subject or source, citation, and object or target. First, one or more complete or incomplete attributes associated with one or more of entities across source, citation and target are identified with a high degree of probable accuracy, wherein such metadata or attributes include but are not limited to, time, language, and location of the entities. The identified attributes are then ascribed or transferred from one entity where the metadata is available to other search entities. Finally, the transferred attributes can be utilized to facilitate the selection and ranking of the cited targets for the search result.

RELATED APPLICATIONS

This application is a continuation of co-pending U.S. application Ser.No. 15/431,075 filed Feb. 13, 2017, which is a continuation ofco-pending U.S. application Ser. No. 14/818,062 filed Aug. 4, 2015, nowissued as U.S. Pat. No. 9,600,586, which is a continuation of U.S.application Ser. No. 13/160,082 filed Jun. 14, 2011, now issued as U.S.Pat. No. 9,129,017, which claims the benefit of U.S. Provisional PatentApplication No. 61/354,562, filed Jun. 14, 2010, and entitled “A systemand method for metadata transfer for search entities,” and is herebyincorporated herein by reference. U.S. application Ser. No. 13/160,082is a continuation-in-part of U.S. application Ser. No. 12/895,593 filedSep. 30, 2010, now issued as U.S. Pat. No. 7,991,725. U.S. applicationSer. No. 13/160,082 is also a continuation-in-part of U.S. applicationSer. No. 12/628,791 filed Dec. 1, 2009, now issued as U.S. Pat. No.8,688,701. U.S. application Ser. No. 13/160,082 is also acontinuation-in-part of U.S. application Ser. No. 12/628,801 filed Dec.1, 2009, now issued as U.S. Pat. No. 8,244,664.

BACKGROUND

Knowledge is increasingly more germane to our exponentially expandinginformation-based society. Perfect knowledge is the ideal thatparticipants seek to assist in decision making and for determiningpreferences, affinities, and dislikes. Practically, perfect knowledgeabout a given topic is virtually impossible to obtain unless theinquirer is the source of all of information about such topic (e.g.,autobiographer). Armed with more information, decision makers aregenerally best positioned to select a choice that will lead to a desiredoutcome/result (e.g., which restaurant to go to for dinner). However, asmore information is becoming readily available through variouselectronic communications modalities (e.g., the Internet), one is leftto sift through what is amounting to a myriad of data to obtain relevantand, more importantly, trust worthy information to assist in decisionmaking activities. Although there are various tools (e.g., searchengines, community boards with various ratings), there lacks any indiciaof personal trustworthiness (e.g., measure of the source's reputationand/or influence) with located data.

Currently, a person seeking to locate information to assist in adecision, to determine an affinity, and/or identify a dislike canleverage traditional non-electronic data sources (e.g., personalrecommendations—which can be few and can be biased) and/or electronicdata sources such as web sites, bulletin boards, blogs, and othersources to locate (sometimes rated) data about a particulartopic/subject (e.g., where to stay when visiting San Francisco). Such anapproach is time consuming and often unreliable as with most of theelectronic data there lacks an indicia of trustworthiness of the sourceof the information. Failing to find a plethora (or spot on) informationfrom immediate non-electronic and/or electronic data source(s), theperson making the inquiry is left to make the decision using limitedinformation, which can lead to less than perfect predictions ofoutcomes, results, and can lead to low levels of satisfactionundertaking one or more activities for which information was sought.

Current practices also do not leverage trustworthiness of informationor, stated differently, attribute a value to the influence of the sourceof data (e.g., referral). With current practices, the entity seeking thedata must make a value judgment on the influence of the data source.Such value judgment is generally based on previous experiences with thedata source (e.g., rely on Mike's restaurant recommendations as he is achef and Laura's hotel recommendations in Europe as she lived and workedin Europe for 5 years). Unless the person making the inquiry has anextensive network of references from which to rely to obtain desireddata needed to make a decision, most often, the person making thedecision is left to take a risk or “roll the dice” based on bestavailable non-attributed (non-reputed) data. Such a prospect often leadscertain participants from not engaging in a contemplated activity.Influence accrued by persons in such a network of references issubjective. In other words, influence accrued by persons in such anetwork of references appear differently to each other person in thenetwork, as each person's opinion is formed by their own individualnetworks of trust.

Real world trust networks follow a small-world pattern, that is, whereeveryone is not connected to everyone else directly, but most people areconnected to most other people through a relatively small number ofintermediaries or “connectors”. Accordingly, this means that someindividuals within the network may disproportionately influence theopinion held by other individuals. In other words, some people'sopinions may be more influential than other people's opinions.

As referred to herein, influence is provided for augmenting reputation,which may be subjective. In some embodiments, influence is provided asan objective measure. For example, influence can be useful in filteringopinions, information, and data. It will be appreciated that reputationand influence provide unique advantages in accordance with someembodiments for the ranking of individuals or products or services ofany type in any means or form. In addition, it would also be desirableif information, attributes, or metadata associated with one entity canbe shared with other related entities in order to be able to identifyrelevant entities during search.

The foregoing examples of the related art and limitations relatedtherewith are intended to be illustrative and not exclusive. Otherlimitations of the related art will become apparent upon a reading ofthe specification and a study of the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example of a citation graph used to support citationsearch.

FIG. 2 depicts an example of a system diagram to support transfer ofattributes among search entities.

FIG. 3 depicts an example of a flowchart of a process to supporttransfer of attributes among search entities.

DETAILED DESCRIPTION OF EMBODIMENTS

The approach is illustrated by way of example and not by way oflimitation in the figures of the accompanying drawings in which likereferences indicate similar elements. It should be noted that referencesto “an” or “one” or “some” embodiment(s) in this disclosure are notnecessarily to the same embodiment, and such references mean at leastone.

A new approach is proposed that contemplates systems and methods toascribe or transfer metadata from one search-related entity to another,where each entity can be one of subject or source, citation, and objector target. As referred to hereinafter, each source or subject can be butis not limited to an internet author or user of social media servicesthat cites a target or object, which can be but is not limited toInternet web sites, blogs, videos, books, films, music, image, video,documents, data files, etc. Each citation may describe, for anon-limiting example, an opinion by a source on a target or object. Eachcitation can be but is not limited to, a Tweet, a blog post, and areview of objects on an Internet web site. First, one or more completeor incomplete attributes associated with one or more of entities acrosssource, citation and target are identified with a high degree ofprobable accuracy, wherein such metadata or attributes include but arenot limited to, time, language, and location of the entities. Theidentified attributes are then ascribed or transferred from one entitywhere the metadata is available to other search entities. Finally, thetransferred attributes can be utilized to facilitate the selection andranking of the cited targets for the search result.

Citation Graph

An illustrative implementation of systems and methods described hereinin accordance with some embodiments includes a citation graph 100 asshown in FIG. 1. In the example of FIG. 1, the citation graph 100comprises a plurality of citations 104, each describing an opinion ofthe object by a source/subject 102. The nodes/entities in the citationgraph 100 are characterized into two categories, 1) subjects 102 capableof having an opinion or creating/making citations 104, in whichexpression of such opinion is explicit, expressed, implicit, or imputedthrough any other technique; and 2) objects 106 cited by citations 104,about which subjects 102 have opinions or make citations. Each subject102 or object 106 in graph 100 represents an influential entity, once aninfluence score for that node has been determined or estimated. Morespecifically, each subject 102 may have an influence score indicatingthe degree to which the subject's opinion influences other subjectsand/or a community of subjects, and each object 106 may have aninfluence score indicating the collective opinions of the plurality ofsubjects 102 citing the object.

In some embodiments, subjects 102 representing any entities or sourcesthat make citations may correspond to one or more of the following:

-   -   Representations of a person, web log, and entities representing        Internet authors or users of social media services including one        or more of the following: blogs, Twitter, or reviews on Internet        web sites;    -   Users of microblogging services such as Twitter;    -   Users of social networks such as MySpace or Facebook, bloggers;    -   Reviewers, who provide expressions of opinion, reviews, or other        information useful for the estimation of influence.

In some embodiments, some subjects/authors 102 who create the citations104 can be related to each other, for a non-limiting example, via aninfluence network or community and influence scores can be assigned tothe subjects 102 based on their authorities in the influence network.

In some embodiments, objects 106 cited by the citations 104 maycorrespond to one or more of the following: Internet web sites, blogs,videos, books, films, music, image, video, documents, data files,objects for sale, objects that are reviewed or recommended or cited,subjects/authors, natural or legal persons, citations, or any entitiesthat are or may be associated with a Uniform Resource Identifier (URI),or any form of product or service or information of any means or formfor which a representation has been made.

In some embodiments, the links or edges 104 of the citation graph 100represent different forms of association between the subject nodes 102and the object nodes 106, such as citations 104 of objects 106 bysubjects 102. For non-limiting examples, citations 104 can be created byauthors citing targets at some point of time and can be one of link,description, keyword or phrase by a source/subject 102 pointing to atarget (subject 102 or object 106). Here, citations may include one ormore of the expression of opinions on objects, expressions of authors inthe form of Tweets, blog posts, reviews of objects on Internet web sitesWikipedia entries, postings to social media such as Twitter or Jaiku,postings to websites, postings in the form of reviews, recommendations,or any other form of citation made to mailing lists, newsgroups,discussion forums, comments to websites or any other form of Internetpublication.

In some embodiments, citations 104 can be made by one subject 102regarding an object 106, such as a recommendation of a website, or arestaurant review, and can be treated as representation an expression ofopinion or description. In some embodiments, citations 104 can be madeby one subject 102 regarding another subject 102, such as arecommendation of one author by another, and can be treated asrepresenting an expression of trustworthiness. In some embodiments,citations 104 can be made by certain object 106 regarding other objects,wherein the object 106 is also a subject.

In some embodiments, citation 104 can be described in the format of(subject, citation description, object, timestamp, type). Citations 104can be categorized into various types based on the characteristics ofsubjects/authors 102, objects/targets 106 and citations 104 themselves.Citations 104 can also reference other citations. The referencerelationship among citations is one of the data sources for discoveringinfluence network.

FIG. 2 depicts an example of a system diagram to support determinationof quality of cited objects in search results based on the influence ofthe citing subjects. Although the diagrams depict components asfunctionally separate, such depiction is merely for illustrativepurposes. It will be apparent that the components portrayed in thisfigure can be arbitrarily combined or divided into separate software,firmware and/or hardware components. Furthermore, it will also beapparent that such components, regardless of how they are combined ordivided, can execute on the same host or multiple hosts, and wherein themultiple hosts can be connected by one or more networks.

In the example of FIG. 2, the system 200 includes at least citationsearch engine 204, influence evaluation engine 204, and object selectionengine 206. As used herein, the term engine refers to software,firmware, hardware, or other component that is used to effectuate apurpose. The engine will typically include software instructions thatare stored in non-volatile memory (also referred to as secondarymemory). When the software instructions are executed, at least a subsetof the software instructions is loaded into memory (also referred to asprimary memory) by a processor. The processor then executes the softwareinstructions in memory. The processor may be a shared processor, adedicated processor, or a combination of shared or dedicated processors.A typical program will include calls to hardware components (such as I/Odevices), which typically requires the execution of drivers. The driversmay or may not be considered part of the engine, but the distinction isnot critical.

In the example of FIG. 2, each of the engines can run on one or morehosting devices (hosts). Here, a host can be a computing device, acommunication device, a storage device, or any electronic device capableof running a software component. For non-limiting examples, a computingdevice can be but is not limited to a laptop PC, a desktop PC, a tabletPC, an iPod, an iPhone, an iPad, Google's Android device, a PDA, or aserver machine. A storage device can be but is not limited to a harddisk drive, a flash memory drive, or any portable storage device. Acommunication device can be but is not limited to a mobile phone.

In the example of FIG. 2, citation search engine 202, influenceevaluation engine 204, and object selection engine 206 each has acommunication interface (not shown), which is a software component thatenables the engines to communicate with each other following certaincommunication protocols, such as TCP/IP protocol, over one or morecommunication networks (not shown). Here, the communication networks canbe but are not limited to, internet, intranet, wide area network (WAN),local area network (LAN), wireless network, Bluetooth, WiFi, and mobilecommunication network. The physical connections of the network and thecommunication protocols are well known to those of skill in the art.

Citation Search

In the example of FIG. 2, citation search engine 202 enables a citationsearch process, which unlike the “classical web search” approaches thatis object/target-centric and focuses only on the relevance of theobjects 106 to the searching criteria, the search process adopted bycitation search engine 202 is “citation” centric, focusing on influenceof the citing subjects 102 that cite the objects. In addition, theclassical web search retrieves and ranks objects 106 based on attributesof the objects, while the proposed search approach adds citation 104 andsubject/author 102 dimensions. The extra metadata associated withsubjects 102, citations 104, and objects 106 provide better rankingcapability, richer functionality and higher efficiency for the searches.

In some embodiments, the citation search/query request processed bycitation search engine 202 may accept and enforce various criteria/termson citation searching, retrieving and ranking, each of which can eitherbe explicitly described by a user or best guessed by the system based oninternal statistical data. Such criteria include but are not limited to,

a) Constraints for the citations, including but are not limited to,

-   -   Description: usually the text search query;    -   Time range of the citations;    -   Author: such as from particular author or sub set of authors;    -   Type: types of citations;        b) Types of the cited objects: the output can be objects,        authors or citations of the types including but are not limited        to,    -   Target types: such as web pages, images, videso, people    -   Author types: such as expert for certain topic    -   Citation types: such as tweets, comments, blog entries        c) Ranking bias of the cited objects: which can be smartly        guessed by the system or specified by user including but are not        limited to,    -   Time bias: recent; point of time; event; general knowledge; auto    -   View point bias: such as general view or perspective of certain        people.    -   Type bias: topic type, target type.

Influence Evaluation

In the example of FIG. 2, influence evaluation engine 204 calculatesinfluence scores of entities (subjects 102 and/or objects 106), whereinsuch influence scores can be used to determine at least in part, incombination with other methods and systems, the ranking of any subset ofobjects 106 obtained from a plurality of citations 104 from citationsearch results.

In some embodiments, influence evaluation engine 204 measures influenceand reputation of subjects 102 that compose the plurality of citations104 citing the plurality of objects 106 on dimensions that are relatedto, for non-limiting examples, one or more of the specific topic orobjects (e.g., automobiles or restaurants) cited by the subjects, orform of citations (e.g., a weblog or Wikipedia entry or news article orTwitter feed), or search terms (e.g., key words or phrases specified inorder to define a subset of all entities that match the search term(s)),in which a subset of the ranked entities are made available based onselection criteria, such as the rank, date or time, orgeography/location associated with the entity, and/or any otherselection criteria.

In some embodiments, influence evaluation engine 204 determines aninfluence score for a first subject or source at least partly based onhow often a first subject is cited or referenced by a (another) secondsubject(s). Here, each of the first or the second subject can be but isnot limited to an internet author or user of social media services,while each citation describes reference by the second subject to acitation of an object by the first subject. The number of the citationsor the citation score of the first subject by the second subjects iscomputed and the influence of the second subjects citing the firstsubject can also be optionally taken into account in the citation score.For a non-limiting example, the influence score of the first subject iscomputed as a function of some or all of: the number of citations of thefirst subject by second subjects, a score for each such citation, andthe influence score of the second subjects. Once computed, the influenceof the first subject as reflected by the count of citations or citationscore of the first subject or subject can be displayed to the user at alocation associated with the first subject, such as the “profile page”of the first subject, together with a list of the second subjects citingthe first subjects, which can be optionally ranked by the influences ofthe second subject.

In some embodiments, influence evaluation engine 204 allows for theattribution of influence on subjects 102 to data sources (e.g., sourcesof opinions, data, or referrals) to be estimated anddistributed/propagated based on the citation graph 100. Morespecifically, an entity can be directly linked to any number of otherentities on any number of dimensions in the citation graph 100, witheach link possibly having an associated score. For a non-limitingexample, a path on a given dimension between two entities, such as asubject 102 and an object 106, includes a directed or an undirected linkfrom the source to an intermediate entity, prefixed to a directed orundirected path from the intermediate entity to the object 106 in thesame or possibly a different dimension.

In some embodiments, influence evaluation engine 204 estimates theinfluence of each entity as the count of actual requests for data,opinion, or searches relating to or originating from other entities,entities with direct links to the entity or with a path in the citationgraph, possibly with a predefined maximum length, to the entity; suchactual requests being counted if they occur within a predefined periodof time and result in the use of the paths originating from the entity(e.g., representing opinions, reviews, citations or other forms ofexpression) with or without the count being adjusted by the possibleweights on each link, the length of each path, and the level of eachentity on each path.

In some embodiments, influence evaluation engine 204 adjusts theinfluence of each entity by metrics relating to the citation graphcomprising all entities or a subset of all linked entities. For anon-limiting example, such metrics can include the density of the graph,defined as the ratio of the number of links to the number of linkedentities in the graph; such metrics are transformed by mathematicalfunctions optimal to the topology of the graph, such as where it isknown that the distribution of links among entities in a given graph maybe non-linear. An example of such an adjustment would be the operationof estimating the influence of an entity as the number of directed linksconnecting to the entity, divided by the logarithm of the density of thecitation graph comprising all linked entities. For example, such anoperation can provide an optimal method of estimating influence rapidlywith a limited degree of computational complexity.

In some embodiments, influence evaluation engine 204 optimizes theestimation of influence for different contexts and requirements ofperformance, memory, graph topology, number of entities, and/or anyother context and/or requirement, by any combination of the operationsdescribed above in paragraphs above, and any similar operationsinvolving metrics including but not limited to values comprising: thenumber of potential source entities to the entity for which influence isto be estimated, the number of potential target entities, the number ofpotential directed paths between any one entity and any other entity onany or all given dimensions, the number of potential directed paths thatinclude the entity, the number of times within a defined period that adirected link from the entity is used for a scoring, search or otheroperation(s).

Metadata Transfer

In the example of FIG. 2, object selection engine 206 ascribes ortransfers metadata or attributes from one search-related entity toanother, where each entity can be one of subject, citation, and objectas depicted by an example of a process in FIG. 3. Although this figuredepicts functional steps in a particular order for purposes ofillustration, the process is not limited to any particular order orarrangement of steps. One skilled in the relevant art will appreciatethat the various steps portrayed in this figure could be omitted,rearranged, combined and/or adapted in various ways.

In the example of FIG. 3, the flowchart 300 starts at block 302 wherecitation searching, retrieving and ranking criteria and mechanisms areset and adjusted based on user specification and/or internal statisticaldata. The flowchart 300 continues to block 304 where a plurality ofcitations of objects that fit the search criteria, such as text match,time filter, author filter, type filter, are retrieved. The flowchart300 continues to block 306 where one or more attributes associated withone or more of entities across source, citation and target areidentified with a high degree of probable accuracy. The flowchart 300continues to block 308 where the identified attributes are ascribed ortransferred from the entities where the attributes are available toother entities. The flowchart 300 ends at block 310 where objects areselected as the search result based on the matching of the objects withthe searching criteria as well as the transferred attributes of theobjects.

In some embodiments, the metadata or attributes of the entitiesidentified and ascribed by the object selection engine 206 can besemantic or descriptive data regarding the entities, including but arenot limited to, timestamp, language, and location of the entities, whichcan be utilized for the selection of the objects.

In the example of FIG. 2, object selection engine 206 utilizes theidentified and ascribed attributes to determine the selection andranking of objects 106 cited by the citations, wherein the objectsinclude but are not limited to documents on the Internet, products,services, data files, legal or natural persons, or any entities in anyform or means that can be searched or cited over a network. For anon-limiting example, the object selection engine 206 may sort thetimestamps of citations of an object and ascribe the earliest timestampto the object as the object's “birth” timestamp, i.e., providing aprobably accurate estimate of when the object was created. For anothernon-limiting example, the object selection engine 206 may utilize thelanguage or location of the subject, if known, to determine the languageof the citation or the location of the object.

In some embodiments, the object selection engine 206 utilizes one of theattributes ascribed among the subjects, citations, and objects asvarious types of content filters to select the objects for the searchresult, wherein such attribute can be one of the properties of theobjects or the citations. For a non-limiting example, if the citationincludes obscene words, the object selection engine 206 may tag theobjects as possibly obscene and the subjects as a source of obscenecontent. Similarly, if an object is flagged by users as obscene, theobject selection engine 206 may also tag the subjects that cited thatobject as sources which cite obscene content.

In some embodiments, in combination with the ascribed attributes ofobjects described above, the object selection engine 206 may alsocalculate and rank the influence scores of the cited objects based onone or more of the following scoring components:

-   -   Subjects of the citations: such as influence scores of the        subjects/authors, expertise of the subjects on the give topic,        perspective bias on the subjects of the citations.    -   Citations: such as text match quality (e.g., content of        citations matching search terms), number of citations, date of        the citations, and other citations related to the same cited        object, time bias, type bias etc.

For a non-limiting example, in the example depicted in FIG. 1, citingsubject Author One has an influence score of 10, which composes Citation1.1 and Citation 1.2, wherein Citation 1.1 cites Target One once whileCitation 1.2 cites Target Two twice; citing subject Author Two has aninfluence score of 5, which composes Citation 2.1, which cites TargetOne three times; citing subject Author Three has an influence score of4, which composes Citation 3.2, which cites Target Two four times. Basedon the influence scores of the authors alone, object selection engine206 calculates the influence score of Target One as 10*1+3*5=25, whilethe influence score of Target Two is calculated as 10*2+4*4=36. SinceTarget Two has a higher influence score than Target One, it should beranked higher than Target One in the final search result.

In some embodiments, object selection engine 206 determines thequalities of the cited objects by examining the distribution ofinfluence scores of subjects citing the objects in the search results.For a non-limiting example, one measure of the influence distribution isthe ratio of the number of citations from the “influential” and the“non-influential” subjects, where “influential” subjects may, for anon-limiting example, have an influence score higher than a thresholddetermined by the percentile distribution of all influence scores.Object selection engine 206 accepts only those objects that show up inthe citation search results if their citation ratios from “influential”and “non-influential” subjects are above a certain threshold whileothers can be marked as spam if the ratio of their citation ratios from“influential” and “non-influential” subjects fall below the certainthreshold, indicating that they are most likely cited from spamsubjects.

In some embodiments, object selection engine 206 calculates and rankscited objects by treating citations of the objects as connections havingpositive or negative weights in a weighted citation graph. A citationwith implicit positive weight can include, for a non-limiting example, aretweet or a link between individual blog posts or web cites, while acitation with negative weight can include, for a non-limiting example, astatement by one subject 102 that another source is a spammer.

In some embodiments, object selection engine 206 uses citations withnegative weights in a citation graph-based rank/influence calculationapproach to propagate negative citation scores through the citationgraph. Assigning and propagating citations of negative weights makes itpossible to identify clusters of spammers in the citation graph withouthaving each spammer individually identified. Furthermore, identifyingsubjects/sources 102 with high influence and propagating a few negativecitations from such subjects is enough to mark an entire cluster ofspammers negatively, thus reducing their influence on the search result.

In some embodiments, object selection engine 206 presents the generatedsearch results of cited objects to a user who issues the search requestor provides the generated search results to a third party for furtherprocessing. In some embodiments, object selection engine 206 presents tothe user a score computed from a function combining the count ofcitations and the influence of the subjects of the citations along withthe search result of the objects. In some embodiments, object selectionengine 206 displays multiple scores computed from functions combiningthe counts of subsets of citations and the influence of the source ofeach citation along with the search result, where each subset may bedetermined by criteria such as the influence of the subjects, orattributes of the subjects or the citations. For non limiting-examples,the following may be displayed to the user—“5 citations from Twitter; 7citations from people in Japan; and 8 citations in English frominfluential users.” The subsets above may be selected and/or filteredeither by the object selection engine 206 or by users.

In some embodiments, object selection engine 206 selects for display ofevery object in the search result, one or more citations and thesubjects of the citations on the basis of criteria such as the recencyor the influence of their citing subjects relative to the othercitations in the search result. Object selection engine 206 thendisplays the selected citations and/or subjects in such a way that therelationship between the search result, the citations and the subjectsof the citations are made transparent to a user.

One embodiment may be implemented using a conventional general purposeor a specialized digital computer or microprocessor(s) programmedaccording to the teachings of the present disclosure, as will beapparent to those skilled in the computer art. Appropriate softwarecoding can readily be prepared by skilled programmers based on theteachings of the present disclosure, as will be apparent to thoseskilled in the software art. The invention may also be implemented bythe preparation of integrated circuits or by interconnecting anappropriate network of conventional component circuits, as will bereadily apparent to those skilled in the art.

One embodiment includes a computer program product which is a machinereadable medium (media) having instructions stored thereon/in which canbe used to program one or more hosts to perform any of the featurespresented herein. The machine readable medium can include, but is notlimited to, one or more types of disks including floppy disks, opticaldiscs, DVD, CD-ROMs, micro drive, and magneto-optical disks, ROMs, RAMs,EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or opticalcards, nanosystems (including molecular memory ICs), or any type ofmedia or device suitable for storing instructions and/or data. Stored onany one of the computer readable medium (media), the present inventionincludes software for controlling both the hardware of the generalpurpose/specialized computer or microprocessor, and for enabling thecomputer or microprocessor to interact with a human viewer or othermechanism utilizing the results of the present invention. Such softwaremay include, but is not limited to, device drivers, operating systems,execution environments/containers, and applications.

The foregoing description of various embodiments of the claimed subjectmatter has been provided for the purposes of illustration anddescription. It is not intended to be exhaustive or to limit the claimedsubject matter to the precise forms disclosed. Many modifications andvariations will be apparent to the practitioner skilled in the art.Particularly, while the concept “interface” is used in the embodimentsof the systems and methods described above, it will be evident that suchconcept can be interchangeably used with equivalent software conceptssuch as, class, method, type, module, component, bean, module, objectmodel, process, thread, and other suitable concepts. While the concept“component” is used in the embodiments of the systems and methodsdescribed above, it will be evident that such concept can beinterchangeably used with equivalent concepts such as, class, method,type, interface, module, object model, and other suitable concepts.Embodiments were chosen and described in order to best describe theprinciples of the invention and its practical application, therebyenabling others skilled in the relevant art to understand the claimedsubject matter, the various embodiments and with various modificationsthat are suited to the particular use contemplated.

1. (canceled)
 2. A non-transitory machine readable medium containingexecutable program instructions which when executed by a data processingdevice cause the device to perform a method comprising: retrieving aplurality of citations that are composed by a plurality of subjectsciting one or more of a plurality of objects that fit searchingcriteria; identifying at least one incomplete attribute from the one ormore of the plurality of objects and at least one identified attributefrom the plurality of the citations corresponding to the at least oneincomplete attribute from the one or more of the plurality of objects;transferring the at least one identified attribute from the plurality ofthe citations to the one or more of the plurality of objects that ishaving the at least one incomplete attribute, wherein the attributes areapplicable to the one of the plurality of objects; selecting a subset ofobjects as a search result; and ranking the subset of objects cited bythe plurality of citations using one or more attributes, wherein the oneor more attributes include the at least one identified attributetransferred from the plurality of citations.
 3. The machine readablemedium of claim 2, wherein: each of the plurality of subjects is one of:representation of a person, web log, and entities representing Internetauthors or users of social media services, user of microbloggingservices, users of social networks, reviewer who provides expressions ofopinion, reviews, or other information useful for the estimation ofinfluence.
 4. The machine readable medium of claim 2, wherein: each ofthe plurality of objects is one of: Internet web sites, blogs, videos,books, films, music, image, video, documents, data files, objects forsale, objects that are reviewed or recommended or cited,subjects/authors, natural or legal persons, citations, or any entitiesthat are associated with a Uniform Resource Identifier (URI).
 5. Themachine readable medium of claim 2, wherein: each of the plurality ofcitations includes one or more of: expression of opinions on theobjects, expressions of authors in the form of Tweets, blog posts,reviews of objects on Internet web sites Wikipedia entries, postings tosocial media, postings to websites, postings in the form of reviews,recommendations, or any other form of citation made to mailing lists,newsgroups, discussion forums, comments to websites or any other form ofInternet publication.
 6. The machine readable medium of claim 2, furthercomprising: accepting and enforcing a plurality of criteria on citationsearching, retrieving and ranking, each of which is either be explicitlydescribed by a user or best guessed by a system based on internalstatistical data.
 7. The machine readable medium of claim 2, furthercomprising: sorting timestamps of the citations of an object andascribing an earliest timestamp to the object as the object's birthtimestamp, which provides an accurate estimate of when the object wascreated.
 8. The machine readable medium of claim 2, further comprising:utilizing known language or location of the subjects to determine thelanguage of the citations or the location of the objects.
 9. The machinereadable medium of claim 2, further comprising: utilizing one of theattributes as a content filter to select the objects for the searchresult, wherein such attribute is one of properties of the objects orthe citations.
 10. The machine readable medium of claim 2, furthercomprising: calculating influence scores of the plurality of subjectsthat compose the plurality of citations citing the plurality of objects.11. The machine readable medium of claim 2, wherein identified attributeis selected from the group consisting of time, location, and language ofat least one of the plurality of citations.
 12. The machine readablemedium of claim 2, wherein the at least one incomplete attribute isselected from the group consisting of a timestamp, language identifier,and location identifier.
 13. A method comprising: retrieving a pluralityof citations that are composed by a plurality of subjects citing one ormore of a plurality of objects that fit searching criteria; identifyingat least one incomplete attribute from the one or more of the pluralityof objects and at least one identified attribute from the plurality ofthe citations corresponding to the at least one incomplete attributefrom the one or more of the plurality of objects; transferring at leastone identified attribute from the plurality of the citations to the oneor more of the plurality of objects that is having the at least oneincomplete attribute, wherein the attributes are applicable to the oneof the plurality of objects; selecting a subset of objects as a searchresult based on a matching of the searching criteria with the at leastone identified attributes transferred to at least one of the pluralityof objects from the citations; and ranking the subset of objects citedby the plurality of.
 14. The method of claim 13, wherein: each of theplurality of subjects is one of: representation of a person, web log,and entities representing Internet authors or users of social mediaservices, user of microblogging services, users of social networks,reviewer who provides expressions of opinion, reviews, or otherinformation useful for the estimation of influence.
 15. The method ofclaim 13, wherein: each of the plurality of objects is one of: Internetweb sites, blogs, videos, books, films, music, image, video, documents,data files, objects for sale, objects that are reviewed or recommendedor cited, subjects/authors, natural or legal persons, citations, or anyentities that are associated with a Uniform Resource Identifier (URI).16. The method of claim 13, wherein: each of the plurality of citationsincludes one or more of: expression of opinions on the objects,expressions of authors in the form of Tweets, blog posts, reviews ofobjects on Internet web sites Wikipedia entries, postings to socialmedia, postings to websites, postings in the form of reviews,recommendations, or any other form of citation made to mailing lists,newsgroups, discussion forums, comments to websites or any other form ofInternet publication.
 17. The method of claim 13, further comprising:accepting and enforcing a plurality of criteria on citation searching,retrieving and ranking, each of which is either be explicitly describedby a user or best guessed by a system based on internal statisticaldata.
 18. The method of claim 13, further comprising: sorting timestampsof the citations of an object and ascribing an earliest timestamp to theobject as the object's birth timestamp, which provides an accurateestimate of when the object was created.
 19. The method of claim 13,further comprising: utilizing known language or location of the subjectsto determine the language of the citations or the location of theobjects.
 20. The method of claim 13, further comprising: utilizing oneof the attributes as a content filter to select the objects for thesearch result, wherein such attribute is one of properties of theobjects or the citations.
 21. The method of claim 13, furthercomprising: calculating influence scores of the plurality of subjectsthat compose the plurality of citations citing the plurality of objects.22. The method of claim 13, wherein the at least one incompleteattribute is selected from the group consisting of a timestamp, languageidentifier, and location identifier.